Calculating posterior distributions and modal estimates in Markov mixture models

Journal of Econometrics

Volumn 75, Issue 1, 1996, Pages 79-97

  Chib, Siddhartha ^a  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

Author keywords

Autoregressive time series; Finite mixture distributions; Gibbs sampling; Hidden Markov models; Markov chain Monte Carlo; Markov switching models; Multivariate normal mixtures; Poisson distribution; Stochastic em algorithm

Indexed keywords

EID: 0003107701     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/0304-4076(95)01770-4     Document Type: Article

References (17)

1. Albert, J. and S. Chib, 1993, Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts, Journal of Business and Economic Statistics 11, 1-15.
2. Baum, L.E., T. Petrie, G. Soules, and N. Weiss, 1970, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Annals of Mathematical Statistics 41, 164-171.
3. Carlin, B. and S. Chib, 1995, Bayesian model choice via Markov chain Monte Carlo, Journal of the Royal Statistical Society B 57, 473-484.
4. Celeux, G. and J. Diebolt, 1985, The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Computational Statistics Quarterly 2, 73-82.
5. Chib, S., 1993, Bayes regression with autoregressive errors: A Gibbs sampling approach, Journal of Econometrics 58, 275-294.
6. Diebolt, J. and C.P. Robert, 1994, Estimation of finite mixture distributions through Bayesian sampling, Journal of the Royal Statistical Society B 56, 363-375.
7. Dempster, A.P., N. Laird, and D.B. Rubin, 1977, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society B 39, 1-38.
8. Everitt, B. and D. Hand, 1981, Finite mixture distributions (Chapman and Hall, London).
9. Gelfand, A.E. and A.F.M. Smith, 1990, Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association 85, 398-409.
10. Goldfeld, S.M. and R.E. Quandt, 1973, A Markov model for switching regressions, Journal of Econometrics 1, 3-16.
11. Hamilton, J.D., 1989, A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica 57, 357-384.
12. Hamilton, J.D., 1994, Time series analysis (Princeton University Press, Princeton, NJ).
13. Leroux, B.G., 1992, Maximum-likelihood estimation for hidden Markov models, Stochastic Processes and their Applications 40, 127-143.
14. Leroux, B.G. and M.L. Puterman, 1992, Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. Biometrics 48, 545-558.
15. McCulloch, R. and R. Tsay, 1994, Statistical analysis of macroeconomic time series via Markov switching models, Journal of Time Series Analysis 15, 523-539.
16. Titterington, D., A.F.M. Smith, and U. Makov, 1985, Statistical analysis of finite mixture distributions (Wiley, New York, NY).
17. Wei, G.C.G. and M.A. Tanner, 1990, A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithm, Journal of the American Statistical Association 85, 699-704.